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installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current 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ibx265-dev_3.5-2_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libmagickcore-6-arch-config_8%3a6.9.11.60+dfsg-1.3ubuntu0.22.04.3_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libcfitsio9_4.0.0-1_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libxext-dev_2%3a1.3.4-1build1_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/automake_1%3a1.16.5-1.3_all.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libsepol-dev_3.3-1build1_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/liblzma-dev_5.2.5-2ubuntu1_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libcairo2-dev_1.16.0-5ubuntu2_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libde265-dev_1.0.8-1_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/gettext_0.21-4ubuntu4_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/imagemagick-6-common_8%3a6.9.11.60+dfsg-1.3ubuntu0.22.04.3_all.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/m4_1.4.18-5ubuntu2_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libbz2-dev_1.0.8-5build1_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libdeflate-dev_1.10-2_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libcgif0_0.2.0-1_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libharfbuzz-dev_2.7.4-1ubuntu3.1_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libopenexr25_2.5.7-1_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/gir1.2-gdkpixbuf-2.0_2.42.8+dfsg-1ubuntu0.2_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libfontconfig1-dev_2.13.1-4.2ubuntu5_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libpcre2-dev_10.39-3ubuntu0.1_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libxdmcp-dev_1%3a1.1.3-0ubuntu5_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libdatrie-dev_0.2.13-2_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/liborc-0.4-dev-bin_1%3a0.4.32-2_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libdjvulibre-text_3.5.28-2build2_all.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libxt-dev_1%3a1.2.1-1_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libgraphite2-dev_1.3.14-1build2_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/libopenjp2-7-dev_2.4.0-6_amd64.deb\n/kaggle/input/libvips-pyvips-installation-and-getting-started/__results___files/__results___18_0.png\n/kaggle/input/libvips-pyvips-installation-and-getting-started/pyvips/pycparser-2.21-py2.py3-none-any.whl\n/kaggle/input/libvips-pyvips-installation-and-getting-started/pyvips/pyvips-2.2.1-py2.py3-none-any.whl\n/kaggle/input/libvips-pyvips-installation-and-getting-started/pyvips/cffi-1.16.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n","output_type":"stream"}],"execution_count":6},{"cell_type":"code","source":"!yes | sudo dpkg -i /kaggle/input/libvips-pyvips-installation-and-getting-started/libvips/*.deb\n!pip install /kaggle/input/libvips-pyvips-installation-and-getting-started/pyvips/pyvips-2.2.1-py2.py3-none-any.whl --no-index --find-links /kaggle/input/libvips-pyvips-installation-and-getting-started/pyvips\n!pip install /kaggle/input/ubc-ocean-dataset/packages/imagesize-1.4.1-py2.py3-none-any.whl","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:53:26.433913Z","iopub.execute_input":"2026-03-25T19:53:26.434174Z","iopub.status.idle":"2026-03-25T19:54:08.234885Z","shell.execute_reply.started":"2026-03-25T19:53:26.434148Z","shell.execute_reply":"2026-03-25T19:54:08.233851Z"}},"outputs":[{"name":"stdout","text":"(Reading database ... 127641 files and directories currently installed.)\nPreparing to unpack .../autoconf_2.71-2_all.deb ...\nUnpacking autoconf (2.71-2) over (2.71-2) ...\nPreparing to unpack .../automake_1%3a1.16.5-1.3_all.deb ...\nUnpacking automake 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unpack .../libpthread-stubs0-dev_0.4-1build2_amd64.deb ...\nUnpacking libpthread-stubs0-dev:amd64 (0.4-1build2) over (0.4-1build2) ...\nPreparing to unpack .../librsvg2-2_2.52.5+dfsg-3ubuntu0.2_amd64.deb ...\nUnpacking librsvg2-2:amd64 (2.52.5+dfsg-3ubuntu0.2) over (2.52.5+dfsg-3ubuntu0.2) ...\nPreparing to unpack .../librsvg2-common_2.52.5+dfsg-3ubuntu0.2_amd64.deb ...\nUnpacking librsvg2-common:amd64 (2.52.5+dfsg-3ubuntu0.2) over (2.52.5+dfsg-3ubuntu0.2) ...\nPreparing to unpack .../librsvg2-dev_2.52.5+dfsg-3ubuntu0.2_amd64.deb ...\nUnpacking librsvg2-dev:amd64 (2.52.5+dfsg-3ubuntu0.2) over (2.52.5+dfsg-3ubuntu0.2) ...\nPreparing to unpack .../libselinux1-dev_3.3-1build2_amd64.deb ...\nUnpacking libselinux1-dev:amd64 (3.3-1build2) over (3.3-1build2) ...\nPreparing to unpack .../libsepol-dev_3.3-1build1_amd64.deb ...\nUnpacking libsepol-dev:amd64 (3.3-1build1) over (3.3-1build1) ...\nPreparing to unpack .../libsigsegv2_2.13-1ubuntu3_amd64.deb ...\nUnpacking libsigsegv2:amd64 (2.13-1ubuntu3) over (2.13-1ubuntu3) ...\nPreparing to unpack .../libsm-dev_2%3a1.2.3-1build2_amd64.deb ...\nUnpacking libsm-dev:amd64 (2:1.2.3-1build2) over (2:1.2.3-1build2) ...\nPreparing to unpack .../libthai-dev_0.1.29-1build1_amd64.deb ...\nUnpacking libthai-dev:amd64 (0.1.29-1build1) over (0.1.29-1build1) ...\nPreparing to unpack .../libtiff-dev_4.3.0-6ubuntu0.6_amd64.deb ...\nUnpacking libtiff-dev:amd64 (4.3.0-6ubuntu0.6) over (4.3.0-6ubuntu0.6) ...\nPreparing to unpack .../libtiff5_4.3.0-6ubuntu0.6_amd64.deb ...\nUnpacking libtiff5:amd64 (4.3.0-6ubuntu0.6) over (4.3.0-6ubuntu0.6) ...\nPreparing to unpack .../libtiffxx5_4.3.0-6ubuntu0.6_amd64.deb ...\nUnpacking libtiffxx5:amd64 (4.3.0-6ubuntu0.6) over (4.3.0-6ubuntu0.6) ...\nPreparing to unpack .../libvips-dev_8.12.1-1build1_amd64.deb ...\nUnpacking libvips-dev (8.12.1-1build1) over (8.12.1-1build1) ...\nPreparing to unpack .../libvips42_8.12.1-1build1_amd64.deb ...\nUnpacking libvips42:amd64 (8.12.1-1build1) over (8.12.1-1build1) ...\nPreparing to unpack .../libwebp-dev_1.2.2-2ubuntu0.22.04.2_amd64.deb ...\nUnpacking libwebp-dev:amd64 (1.2.2-2ubuntu0.22.04.2) over (1.2.2-2ubuntu0.22.04.2) ...\nPreparing to unpack .../libwebp7_1.2.2-2ubuntu0.22.04.2_amd64.deb ...\nUnpacking libwebp7:amd64 (1.2.2-2ubuntu0.22.04.2) over (1.2.2-2ubuntu0.22.04.2) ...\nPreparing to unpack .../libwebpdemux2_1.2.2-2ubuntu0.22.04.2_amd64.deb ...\nUnpacking libwebpdemux2:amd64 (1.2.2-2ubuntu0.22.04.2) over (1.2.2-2ubuntu0.22.04.2) ...\nPreparing to unpack .../libwebpmux3_1.2.2-2ubuntu0.22.04.2_amd64.deb ...\nUnpacking libwebpmux3:amd64 (1.2.2-2ubuntu0.22.04.2) over (1.2.2-2ubuntu0.22.04.2) ...\nPreparing to unpack .../libwmf-0.2-7_0.2.12-5ubuntu1_amd64.deb ...\nUnpacking libwmf-0.2-7:amd64 (0.2.12-5ubuntu1) over (0.2.12-5ubuntu1) ...\nPreparing to unpack .../libwmf-dev_0.2.12-5ubuntu1_amd64.deb ...\nUnpacking libwmf-dev (0.2.12-5ubuntu1) over (0.2.12-5ubuntu1) ...\nPreparing to unpack .../libwmflite-0.2-7_0.2.12-5ubuntu1_amd64.deb ...\nUnpacking libwmflite-0.2-7:amd64 (0.2.12-5ubuntu1) over (0.2.12-5ubuntu1) ...\nPreparing to unpack .../libx11-6_2%3a1.7.5-1ubuntu0.3_amd64.deb ...\nUnpacking libx11-6:amd64 (2:1.7.5-1ubuntu0.3) over (2:1.7.5-1ubuntu0.3) ...\nPreparing to unpack .../libx11-dev_2%3a1.7.5-1ubuntu0.3_amd64.deb ...\nUnpacking libx11-dev:amd64 (2:1.7.5-1ubuntu0.3) over (2:1.7.5-1ubuntu0.3) ...\nPreparing to unpack .../libx11-xcb1_2%3a1.7.5-1ubuntu0.3_amd64.deb ...\nUnpacking libx11-xcb1:amd64 (2:1.7.5-1ubuntu0.3) over (2:1.7.5-1ubuntu0.3) ...\nPreparing to unpack .../libx265-dev_3.5-2_amd64.deb ...\nUnpacking libx265-dev:amd64 (3.5-2) over (3.5-2) ...\nPreparing to unpack .../libxau-dev_1%3a1.0.9-1build5_amd64.deb ...\nUnpacking libxau-dev:amd64 (1:1.0.9-1build5) over (1:1.0.9-1build5) ...\nPreparing to unpack .../libxcb-render0-dev_1.14-3ubuntu3_amd64.deb ...\nUnpacking libxcb-render0-dev:amd64 (1.14-3ubuntu3) over (1.14-3ubuntu3) ...\nPreparing to unpack .../libxcb-shm0-dev_1.14-3ubuntu3_amd64.deb ...\nUnpacking libxcb-shm0-dev:amd64 (1.14-3ubuntu3) over (1.14-3ubuntu3) ...\nPreparing to unpack .../libxcb1-dev_1.14-3ubuntu3_amd64.deb ...\nUnpacking libxcb1-dev:amd64 (1.14-3ubuntu3) over (1.14-3ubuntu3) ...\nPreparing to unpack .../libxdmcp-dev_1%3a1.1.3-0ubuntu5_amd64.deb ...\nUnpacking libxdmcp-dev:amd64 (1:1.1.3-0ubuntu5) over (1:1.1.3-0ubuntu5) ...\nPreparing to unpack .../libxext-dev_2%3a1.3.4-1build1_amd64.deb ...\nUnpacking libxext-dev:amd64 (2:1.3.4-1build1) over (2:1.3.4-1build1) ...\nPreparing to unpack .../libxft-dev_2.3.4-1_amd64.deb ...\nUnpacking libxft-dev:amd64 (2.3.4-1) over (2.3.4-1) ...\nPreparing to unpack .../libxml2-dev_2.9.13+dfsg-1ubuntu0.3_amd64.deb ...\nUnpacking libxml2-dev:amd64 (2.9.13+dfsg-1ubuntu0.3) over (2.9.13+dfsg-1ubuntu0.3) ...\nPreparing to unpack .../libxrender-dev_1%3a0.9.10-1build4_amd64.deb ...\nUnpacking libxrender-dev:amd64 (1:0.9.10-1build4) over (1:0.9.10-1build4) ...\nPreparing to unpack .../libxt-dev_1%3a1.2.1-1_amd64.deb ...\nUnpacking libxt-dev:amd64 (1:1.2.1-1) over (1:1.2.1-1) ...\nPreparing to unpack .../m4_1.4.18-5ubuntu2_amd64.deb ...\nUnpacking m4 (1.4.18-5ubuntu2) over (1.4.18-5ubuntu2) ...\nPreparing to unpack .../pango1.0-tools_1.50.6+ds-2ubuntu1_amd64.deb ...\nUnpacking pango1.0-tools (1.50.6+ds-2ubuntu1) over (1.50.6+ds-2ubuntu1) ...\nPreparing to unpack .../uuid-dev_2.37.2-4ubuntu3_amd64.deb ...\nUnpacking uuid-dev:amd64 (2.37.2-4ubuntu3) over (2.37.2-4ubuntu3) ...\nPreparing to unpack .../x11proto-dev_2021.5-1_all.deb ...\nUnpacking x11proto-dev (2021.5-1) over (2021.5-1) ...\nPreparing to unpack .../xorg-sgml-doctools_1%3a1.11-1.1_all.deb ...\nUnpacking xorg-sgml-doctools (1:1.11-1.1) over (1:1.11-1.1) ...\nPreparing to unpack .../xtrans-dev_1.4.0-1_all.deb ...\nUnpacking xtrans-dev (1.4.0-1) over (1.4.0-1) ...\nSetting up autotools-dev (20220109.1) ...\nSetting up gettext-base (0.21-4ubuntu4) ...\nSetting up gettext (0.21-4ubuntu4) ...\nSetting up gir1.2-freedesktop:amd64 (1.72.0-1) ...\nSetting up gir1.2-gdkpixbuf-2.0:amd64 (2.42.8+dfsg-1ubuntu0.2) ...\nSetting up gir1.2-harfbuzz-0.0:amd64 (2.7.4-1ubuntu3.1) ...\nSetting up icu-devtools (70.1-2) ...\nSetting up imagemagick-6-common (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.3) ...\nSetting up libaom-dev:amd64 (3.3.0-1) ...\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libblkid-dev:amd64:\n libblkid-dev:amd64 depends on libblkid1 (= 2.37.2-4ubuntu3); however:\n  Version of libblkid1:amd64 on system is 2.37.2-4ubuntu3.4.\n\n\u001b[1mdpkg:\u001b[0m error processing package libblkid-dev:amd64 (--install):\n dependency problems - leaving unconfigured\nSetting up libbz2-dev:amd64 (1.0.8-5build1) ...\nSetting up libcfitsio9:amd64 (4.0.0-1) ...\nSetting up libcgif0 (0.2.0-1) ...\nSetting up libdatrie-dev:amd64 (0.2.13-2) ...\nSetting up libdav1d-dev:amd64 (0.9.2-1) ...\nSetting up libde265-0:amd64 (1.0.8-1) ...\nSetting up libde265-dev:amd64 (1.0.8-1) ...\nSetting up libdeflate-dev:amd64 (1.10-2) ...\nSetting up libdjvulibre-text (3.5.28-2build2) ...\nSetting up libdjvulibre21:amd64 (3.5.28-2build2) ...\nSetting up libexif12:amd64 (0.6.24-1build1) ...\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libexpat1-dev:amd64:\n libexpat1-dev:amd64 depends on libexpat1 (= 2.4.7-1ubuntu0.2); however:\n  Version of libexpat1:amd64 on system is 2.4.7-1ubuntu0.3.\n\n\u001b[1mdpkg:\u001b[0m error processing package libexpat1-dev:amd64 (--install):\n dependency problems - leaving unconfigured\nSetting up libffi-dev:amd64 (3.4.2-4) ...\nSetting up libfftw3-double3:amd64 (3.3.8-2ubuntu8) ...\nSetting up libfftw3-long3:amd64 (3.3.8-2ubuntu8) ...\nSetting up libfftw3-quad3:amd64 (3.3.8-2ubuntu8) ...\nSetting up libfftw3-single3:amd64 (3.3.8-2ubuntu8) ...\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libfontconfig-dev:amd64:\n libfontconfig-dev:amd64 depends on libexpat1-dev; however:\n  Package libexpat1-dev:amd64 is not configured yet.\n\n\u001b[1mdpkg:\u001b[0m error processing package libfontconfig-dev:amd64 (--install):\n dependency problems - leaving unconfigured\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libfontconfig1-dev:amd64:\n libfontconfig1-dev:amd64 depends on libfontconfig-dev (= 2.13.1-4.2ubuntu5); however:\n  Package libfontconfig-dev:amd64 is not configured yet.\n\n\u001b[1mdpkg:\u001b[0m error processing package libfontconfig1-dev:amd64 (--install):\n dependency problems - leaving unconfigured\nSetting up libfribidi-dev:amd64 (1.0.8-2ubuntu3.1) ...\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libgdk-pixbuf-2.0-dev:amd64:\n libgdk-pixbuf-2.0-dev:amd64 depends on libgdk-pixbuf-2.0-0 (= 2.42.8+dfsg-1ubuntu0.2); however:\n  Version of libgdk-pixbuf-2.0-0:amd64 on system is 2.42.8+dfsg-1ubuntu0.3.\n\n\u001b[1mdpkg:\u001b[0m error processing package libgdk-pixbuf-2.0-dev:amd64 (--install):\n dependency problems - leaving unconfigured\nSetting up libgdk-pixbuf2.0-bin (2.42.8+dfsg-1ubuntu0.2) ...\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libgif-dev:\n libgif-dev depends on libgif7 (= 5.1.9-2build2); however:\n  Version of libgif7:amd64 on system is 5.1.9-2ubuntu0.1.\n\n\u001b[1mdpkg:\u001b[0m error processing package libgif-dev (--install):\n dependency problems - leaving unconfigured\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libglib2.0-dev-bin:\n libglib2.0-dev-bin depends on libglib2.0-0 (= 2.72.4-0ubuntu2.2); however:\n  Version of libglib2.0-0:amd64 on system is 2.72.4-0ubuntu2.3.\n\n\u001b[1mdpkg:\u001b[0m error processing package libglib2.0-dev-bin (--install):\n dependency problems - leaving unconfigured\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libglib2.0-dev:amd64:\n libglib2.0-dev:amd64 depends on libglib2.0-0 (= 2.72.4-0ubuntu2.2); however:\n  Version of libglib2.0-0:amd64 on system is 2.72.4-0ubuntu2.3.\n libglib2.0-dev:amd64 depends on libglib2.0-bin (= 2.72.4-0ubuntu2.2); however:\n  Version of libglib2.0-bin on system is 2.72.4-0ubuntu2.3.\n libglib2.0-dev:amd64 depends on libglib2.0-dev-bin (= 2.72.4-0ubuntu2.2); however:\n  Package libglib2.0-dev-bin is not configured yet.\n\n\u001b[1mdpkg:\u001b[0m error processing package libglib2.0-dev:amd64 (--install):\n dependency problems - leaving unconfigured\nSetting up libgraphite2-dev:amd64 (1.3.14-1build2) ...\nSetting up libgsf-1-common (1.14.47-1build2) ...\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libgsf-1-dev:amd64:\n libgsf-1-dev:amd64 depends on libglib2.0-dev; however:\n  Package libglib2.0-dev:amd64 is not configured yet.\n\n\u001b[1mdpkg:\u001b[0m error processing package libgsf-1-dev:amd64 (--install):\n dependency problems - leaving unconfigured\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libharfbuzz-dev:amd64:\n libharfbuzz-dev:amd64 depends on libglib2.0-dev (>= 2.19.1); however:\n  Package libglib2.0-dev:amd64 is not configured yet.\n\n\u001b[1mdpkg:\u001b[0m error processing package libharfbuzz-dev:amd64 (--install):\n dependency problems - leaving unconfigured\nSetting up libharfbuzz-gobject0:amd64 (2.7.4-1ubuntu3.1) ...\nSetting up libharfbuzz-icu0:amd64 (2.7.4-1ubuntu3.1) ...\nSetting up libheif1:amd64 (1.12.0-2build1) ...\nSetting up libicu-dev:amd64 (70.1-2) ...\nSetting up libilmbase25:amd64 (2.5.7-2) ...\nSetting up libimagequant0:amd64 (2.17.0-1) ...\nSetting up libjbig-dev:amd64 (2.1-3.1ubuntu0.22.04.1) ...\nSetting up liblcms2-dev:amd64 (2.12~rc1-2build2) ...\nSetting up liblqr-1-0:amd64 (0.4.2-2.1) ...\nSetting up liblzma-dev:amd64 (5.2.5-2ubuntu1) ...\nSetting up liblzo2-2:amd64 (2.10-2build3) ...\nSetting up libmagickcore-6-headers (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.3) ...\nSetting up libmagickwand-6-headers (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.3) ...\nSetting up libmatio11:amd64 (1.5.21-1) ...\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libmount-dev:amd64:\n libmount-dev:amd64 depends on libblkid-dev; however:\n  Package libblkid-dev:amd64 is not configured yet.\n libmount-dev:amd64 depends on libmount1 (= 2.37.2-4ubuntu3); however:\n  Version of libmount1:amd64 on system is 2.37.2-4ubuntu3.4.\n\n\u001b[1mdpkg:\u001b[0m error processing package libmount-dev:amd64 (--install):\n dependency problems - leaving unconfigured\nSetting up libopenexr25:amd64 (2.5.7-1) ...\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libopenjp2-7-dev:amd64:\n libopenjp2-7-dev:amd64 depends on libopenjp2-7 (= 2.4.0-6); however:\n  Version of libopenjp2-7:amd64 on system is 2.4.0-6ubuntu0.1.\n\n\u001b[1mdpkg:\u001b[0m error processing package libopenjp2-7-dev:amd64 (--install):\n dependency problems - leaving unconfigured\nSetting up libopenslide-dev (3.4.1+dfsg-5build1) ...\nSetting up liborc-0.4-0:amd64 (1:0.4.32-2) ...\nSetting up liborc-0.4-dev-bin (1:0.4.32-2) ...\nSetting up liborc-0.4-dev:amd64 (1:0.4.32-2) ...\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libpango1.0-dev:amd64:\n libpango1.0-dev:amd64 depends on libfontconfig-dev (>= 2.13.0); however:\n  Package libfontconfig-dev:amd64 is not configured yet.\n libpango1.0-dev:amd64 depends on libglib2.0-dev (>= 2.62.0); however:\n  Package libglib2.0-dev:amd64 is not configured yet.\n libpango1.0-dev:amd64 depends on libharfbuzz-dev (>= 2.6.0); however:\n  Package libharfbuzz-dev:amd64 is not configured yet.\n\n\u001b[1mdpkg:\u001b[0m error processing package libpango1.0-dev:amd64 (--install):\n dependency problems - leaving unconfigured\nSetting up libpcre16-3:amd64 (2:8.39-13ubuntu0.22.04.1) ...\nSetting up libpcre2-16-0:amd64 (10.39-3ubuntu0.1) ...\nSetting up libpcre2-32-0:amd64 (10.39-3ubuntu0.1) ...\nSetting up libpcre2-posix3:amd64 (10.39-3ubuntu0.1) ...\nSetting up libpcre32-3:amd64 (2:8.39-13ubuntu0.22.04.1) ...\nSetting up libpcrecpp0v5:amd64 (2:8.39-13ubuntu0.22.04.1) ...\nSetting up libpixman-1-dev:amd64 (0.40.0-1ubuntu0.22.04.1) ...\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libpoppler-glib-dev:\n libpoppler-glib-dev depends on libglib2.0-dev (>= 2.41); however:\n  Package libglib2.0-dev:amd64 is not configured yet.\n\n\u001b[1mdpkg:\u001b[0m error processing package libpoppler-glib-dev (--install):\n dependency problems - leaving unconfigured\nSetting up libpthread-stubs0-dev:amd64 (0.4-1build2) ...\nSetting up librsvg2-2:amd64 (2.52.5+dfsg-3ubuntu0.2) ...\nSetting up librsvg2-common:amd64 (2.52.5+dfsg-3ubuntu0.2) ...\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of librsvg2-dev:amd64:\n librsvg2-dev:amd64 depends on libgdk-pixbuf-2.0-dev (>= 2.23.5-2) | libgdk-pixbuf2.0-dev (>= 2.23.5-2); however:\n  Package libgdk-pixbuf-2.0-dev:amd64 is not configured yet.\n  Package libgdk-pixbuf2.0-dev is not installed.\n librsvg2-dev:amd64 depends on libglib2.0-dev (>= 2.50.0); however:\n  Package libglib2.0-dev:amd64 is not configured yet.\n\n\u001b[1mdpkg:\u001b[0m error processing package librsvg2-dev:amd64 (--install):\n dependency problems - leaving unconfigured\nSetting up libsepol-dev:amd64 (3.3-1build1) ...\nSetting up libsigsegv2:amd64 (2.13-1ubuntu3) ...\nSetting up libthai-dev:amd64 (0.1.29-1build1) ...\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libvips-dev:\n libvips-dev depends on libpango1.0-dev; however:\n  Package libpango1.0-dev:amd64 is not configured yet.\n libvips-dev depends on libfontconfig1-dev; however:\n  Package libfontconfig1-dev:amd64 is not configured yet.\n  Package libfontconfig-dev:amd64 which provides libfontconfig1-dev is not configured yet.\n libvips-dev depends on libglib2.0-dev; however:\n  Package libglib2.0-dev:amd64 is not configured yet.\n libvips-dev depends on libexpat1-dev; however:\n  Package libexpat1-dev:amd64 is not configured yet.\n libvips-dev depends on libgsf-1-dev; however:\n  Package libgsf-1-dev:amd64 is not configured yet.\n libvips-dev depends on libgif-dev (>= 5.1); however:\n  Package libgif-dev is not configured yet.\n libvips-dev depends on libpoppler-glib-dev; however:\n  Package libpoppler-glib-dev is not configured yet.\n libvips-dev depends on librsvg2-dev (>= 2.40.0); however:\n  Package librsvg2-dev:amd64 is not configured yet.\n\n\u001b[1mdpkg:\u001b[0m error processing package libvips-dev (--install):\n dependency problems - leaving unconfigured\nSetting up libwebp7:amd64 (1.2.2-2ubuntu0.22.04.2) ...\nSetting up libwebpdemux2:amd64 (1.2.2-2ubuntu0.22.04.2) ...\nSetting up libwebpmux3:amd64 (1.2.2-2ubuntu0.22.04.2) ...\nSetting up libwmflite-0.2-7:amd64 (0.2.12-5ubuntu1) ...\nSetting up libx11-6:amd64 (2:1.7.5-1ubuntu0.3) ...\nSetting up libx11-xcb1:amd64 (2:1.7.5-1ubuntu0.3) ...\nSetting up libx265-dev:amd64 (3.5-2) ...\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libxft-dev:amd64:\n libxft-dev:amd64 depends on libfontconfig1-dev; however:\n  Package libfontconfig1-dev:amd64 is not configured yet.\n  Package libfontconfig-dev:amd64 which provides libfontconfig1-dev is not configured yet.\n\n\u001b[1mdpkg:\u001b[0m error processing package libxft-dev:amd64 (--install):\n dependency problems - leaving unconfigured\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libxml2-dev:amd64:\n libxml2-dev:amd64 depends on libxml2 (= 2.9.13+dfsg-1ubuntu0.3); however:\n  Version of libxml2:amd64 on system is 2.9.13+dfsg-1ubuntu0.4.\n\n\u001b[1mdpkg:\u001b[0m error processing package libxml2-dev:amd64 (--install):\n dependency problems - leaving unconfigured\nSetting up m4 (1.4.18-5ubuntu2) ...\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of uuid-dev:amd64:\n uuid-dev:amd64 depends on libuuid1 (= 2.37.2-4ubuntu3); however:\n  Version of libuuid1:amd64 on system is 2.37.2-4ubuntu3.4.\n\n\u001b[1mdpkg:\u001b[0m error processing package uuid-dev:amd64 (--install):\n dependency problems - leaving unconfigured\nSetting up xorg-sgml-doctools (1:1.11-1.1) ...\nSetting up xtrans-dev (1.4.0-1) ...\nSetting up autoconf (2.71-2) ...\nSetting up automake (1:1.16.5-1.3) ...\nSetting up gir1.2-rsvg-2.0:amd64 (2.52.5+dfsg-3ubuntu0.2) ...\nSetting up libcairo-script-interpreter2:amd64 (1.16.0-5ubuntu2) ...\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libcairo2-dev:amd64:\n libcairo2-dev:amd64 depends on libfontconfig1-dev (>= 2.2.95); however:\n  Package libfontconfig1-dev:amd64 is not configured yet.\n  Package libfontconfig-dev:amd64 which provides libfontconfig1-dev is not configured yet.\n libcairo2-dev:amd64 depends on libglib2.0-dev; however:\n  Package libglib2.0-dev:amd64 is not configured yet.\n\n\u001b[1mdpkg:\u001b[0m error processing package libcairo2-dev:amd64 (--install):\n dependency problems - leaving unconfigured\nSetting up libcfitsio-dev:amd64 (4.0.0-1) ...\nSetting up libcgif-dev (0.2.0-1) ...\nSetting up libdjvulibre-dev:amd64 (3.5.28-2build2) ...\nSetting up libexif-dev:amd64 (0.6.24-1build1) ...\nSetting up libfftw3-bin (3.3.8-2ubuntu8) ...\nSetting up libfftw3-dev:amd64 (3.3.8-2ubuntu8) ...\nSetting up libgsf-1-114:amd64 (1.14.47-1build2) ...\nSetting up libheif-dev:amd64 (1.12.0-2build1) ...\nSetting up libilmbase-dev:amd64 (2.5.7-2) ...\nSetting up libimagequant-dev:amd64 (2.17.0-1) ...\nSetting up liblqr-1-0-dev:amd64 (0.4.2-2.1) ...\nSetting up libltdl-dev:amd64 (2.4.6-15build2) ...\nSetting up libmagickcore-6-arch-config:amd64 (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.3) ...\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libmagickcore-6.q16-dev:amd64:\n libmagickcore-6.q16-dev:amd64 depends on libopenjp2-7-dev; however:\n  Package libopenjp2-7-dev:amd64 is not configured yet.\n libmagickcore-6.q16-dev:amd64 depends on librsvg2-dev; however:\n  Package librsvg2-dev:amd64 is not configured yet.\n libmagickcore-6.q16-dev:amd64 depends on libxml2-dev; however:\n  Package libxml2-dev:amd64 is not configured yet.\n\n\u001b[1mdpkg:\u001b[0m error processing package libmagickcore-6.q16-dev:amd64 (--install):\n dependency problems - leaving unconfigured\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libmagickcore-dev:\n libmagickcore-dev depends on libmagickcore-6.q16-dev; however:\n  Package libmagickcore-6.q16-dev:amd64 is not configured yet.\n\n\u001b[1mdpkg:\u001b[0m error processing package libmagickcore-dev (--install):\n dependency problems - leaving unconfigured\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libmagickwand-6.q16-dev:amd64:\n libmagickwand-6.q16-dev:amd64 depends on libmagickcore-6.q16-dev (= 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.3); however:\n  Package libmagickcore-6.q16-dev:amd64 is not configured yet.\n\n\u001b[1mdpkg:\u001b[0m error processing package libmagickwand-6.q16-dev:amd64 (--install):\n dependency problems - leaving unconfigured\n\u001b[1mdpkg:\u001b[0m dependency problems prevent configuration of libmagickwand-dev:\n libmagickwand-dev depends on libmagickwand-6.q16-dev; however:\n  Package libmagickwand-6.q16-dev:amd64 is not configured yet.\n\n\u001b[1mdpkg:\u001b[0m error processing package libmagickwand-dev (--install):\n dependency problems - leaving unconfigured\nSetting up libmatio-dev:amd64 (1.5.21-1) ...\nSetting up libopenexr-dev (2.5.7-1) ...\nSetting up libpangoxft-1.0-0:amd64 (1.50.6+ds-2ubuntu1) ...\nSetting up libpcre2-dev:amd64 (10.39-3ubuntu0.1) ...\nSetting up libpcre3-dev:amd64 (2:8.39-13ubuntu0.22.04.1) ...\nSetting up libselinux1-dev:amd64 (3.3-1build2) ...\nSetting up libtiff5:amd64 (4.3.0-6ubuntu0.6) ...\nSetting up libtiffxx5:amd64 (4.3.0-6ubuntu0.6) ...\nSetting up libwebp-dev:amd64 (1.2.2-2ubuntu0.22.04.2) ...\nSetting up libwmf-0.2-7:amd64 (0.2.12-5ubuntu1) ...\nSetting up pango1.0-tools (1.50.6+ds-2ubuntu1) ...\nSetting up x11proto-dev (2021.5-1) ...\nSetting up gir1.2-gsf-1:amd64 (1.14.47-1build2) ...\nSetting up gir1.2-pango-1.0:amd64 (1.50.6+ds-2ubuntu1) ...\nSetting up libice-dev:amd64 (2:1.0.10-1build2) ...\nSetting up libmagickcore-6.q16-6:amd64 (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.3) ...\nSetting up libmagickwand-6.q16-6:amd64 (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.3) ...\nSetting up libpoppler118:amd64 (22.02.0-2ubuntu0.2) ...\nSetting up libsm-dev:amd64 (2:1.2.3-1build2) ...\nSetting up libtiff-dev:amd64 (4.3.0-6ubuntu0.6) ...\nSetting up libxau-dev:amd64 (1:1.0.9-1build5) ...\nSetting up libxdmcp-dev:amd64 (1:1.1.3-0ubuntu5) ...\nSetting up libmagickcore-6.q16-6-extra:amd64 (8:6.9.11.60+dfsg-1.3ubuntu0.22.04.3) ...\nSetting up libpoppler-dev:amd64 (22.02.0-2ubuntu0.2) ...\nSetting up libpoppler-glib8:amd64 (22.02.0-2ubuntu0.2) ...\nSetting up libvips42:amd64 (8.12.1-1build1) ...\nSetting up libxcb1-dev:amd64 (1.14-3ubuntu3) ...\nSetting up gir1.2-poppler-0.18:amd64 (22.02.0-2ubuntu0.2) ...\nSetting up gir1.2-vips-8.0 (8.12.1-1build1) ...\nSetting up libx11-dev:amd64 (2:1.7.5-1ubuntu0.3) ...\nSetting up libxcb-render0-dev:amd64 (1.14-3ubuntu3) ...\nSetting up libxcb-shm0-dev:amd64 (1.14-3ubuntu3) ...\nSetting up libxext-dev:amd64 (2:1.3.4-1build1) ...\nSetting up libxrender-dev:amd64 (1:0.9.10-1build4) ...\nSetting up libxt-dev:amd64 (1:1.2.1-1) ...\nSetting up libwmf-dev (0.2.12-5ubuntu1) ...\nProcessing triggers for man-db (2.10.2-1) ...\nProcessing triggers for install-info (6.8-4build1) ...\nProcessing triggers for libc-bin (2.35-0ubuntu3.8) ...\nProcessing triggers for libglib2.0-0:amd64 (2.72.4-0ubuntu2.3) ...\nProcessing triggers for libgdk-pixbuf-2.0-0:amd64 (2.42.8+dfsg-1ubuntu0.3) ...\nErrors were encountered while processing:\n libblkid-dev:amd64\n libexpat1-dev:amd64\n libfontconfig-dev:amd64\n libfontconfig1-dev:amd64\n libgdk-pixbuf-2.0-dev:amd64\n libgif-dev\n libglib2.0-dev-bin\n libglib2.0-dev:amd64\n libgsf-1-dev:amd64\n libharfbuzz-dev:amd64\n libmount-dev:amd64\n libopenjp2-7-dev:amd64\n libpango1.0-dev:amd64\n libpoppler-glib-dev\n librsvg2-dev:amd64\n libvips-dev\n libxft-dev:amd64\n libxml2-dev:amd64\n uuid-dev:amd64\n libcairo2-dev:amd64\n libmagickcore-6.q16-dev:amd64\n libmagickcore-dev\n libmagickwand-6.q16-dev:amd64\n libmagickwand-dev\nyes: standard output: Broken pipe\nLooking in links: /kaggle/input/libvips-pyvips-installation-and-getting-started/pyvips\nProcessing /kaggle/input/libvips-pyvips-installation-and-getting-started/pyvips/pyvips-2.2.1-py2.py3-none-any.whl\nRequirement already satisfied: cffi>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from pyvips==2.2.1) (1.16.0)\nRequirement already satisfied: pycparser in /opt/conda/lib/python3.10/site-packages (from cffi>=1.0.0->pyvips==2.2.1) (2.22)\npyvips is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\nProcessing /kaggle/input/ubc-ocean-dataset/packages/imagesize-1.4.1-py2.py3-none-any.whl\nimagesize is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"import os\nimport yaml\nfrom pathlib import Path\nfrom tqdm import tqdm\nimport numpy as np\nimport pandas as pd\nos.environ['OPENCV_IO_MAX_IMAGE_PIXELS'] = str(pow(2, 40))\nimport cv2\nimport pyvips\nimport imagesize\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport timm\nimport albumentations as A\nfrom albumentations.pytorch.transforms import ToTensorV2\nimport matplotlib.pyplot as plt","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.236453Z","iopub.execute_input":"2026-03-25T19:54:08.236841Z","iopub.status.idle":"2026-03-25T19:54:08.242623Z","shell.execute_reply.started":"2026-03-25T19:54:08.236802Z","shell.execute_reply":"2026-03-25T19:54:08.24182Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"competition_dataset = Path('/kaggle/input/competitions/UBC-OCEAN')\nexternal_dataset = Path('/kaggle/input/ubc-ocean-dataset')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T20:02:06.150847Z","iopub.execute_input":"2026-03-25T20:02:06.151455Z","iopub.status.idle":"2026-03-25T20:02:06.155593Z","shell.execute_reply.started":"2026-03-25T20:02:06.15142Z","shell.execute_reply":"2026-03-25T20:02:06.154678Z"}},"outputs":[],"execution_count":15},{"cell_type":"code","source":"# Decide which test.csv is available\nif (competition_dataset / 'test.csv').exists():\n    df = pd.read_csv(competition_dataset / 'test.csv')\n    image_folder = competition_dataset / 'test_images'\n    print(\"Using competition test.csv\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T20:02:36.520468Z","iopub.execute_input":"2026-03-25T20:02:36.521149Z","iopub.status.idle":"2026-03-25T20:02:36.525195Z","shell.execute_reply.started":"2026-03-25T20:02:36.521115Z","shell.execute_reply":"2026-03-25T20:02:36.524434Z"}},"outputs":[],"execution_count":17},{"cell_type":"code","source":"# Decide which test.csv is available\nif (competition_dataset / 'test.csv').exists():\n    df = pd.read_csv(competition_dataset / 'test.csv')\n    image_folder = competition_dataset / 'test_images'\n    print(\"Using competition test.csv\")\n\nelse:\n    df = pd.read_csv(external_dataset / 'test.csv')\n    image_folder = external_dataset / 'test_images'\n    print(\"Using external test.csv\")\n\n# Submission check\nis_submission = df.shape[0] != 1\n\n# Image type (WSI vs TMA)\ndf['image_type'] = 'wsi'\ndf.loc[(df['image_width'] < 4000) & (df['image_height'] < 4000), 'image_type'] = 'tma'\n\n# Image path\ndf['image_path'] = df['image_id'].apply(lambda x: str(image_folder / f'{x}.png'))\n\nprint(f'Dataset Shape: {df.shape}')\ndf.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T20:03:31.064796Z","iopub.execute_input":"2026-03-25T20:03:31.065536Z","iopub.status.idle":"2026-03-25T20:03:31.18808Z","shell.execute_reply.started":"2026-03-25T20:03:31.065502Z","shell.execute_reply":"2026-03-25T20:03:31.186826Z"}},"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[18], line 8\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing competition test.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m----> 8\u001b[0m     df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexternal_dataset\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtest.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      9\u001b[0m     image_folder \u001b[38;5;241m=\u001b[39m external_dataset \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtest_images\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     10\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing external test.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m   1014\u001b[0m     dialect,\n\u001b[1;32m   1015\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m   1023\u001b[0m )\n\u001b[1;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m 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1884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1887\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    869\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    870\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    871\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    872\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    880\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    881\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    882\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/kaggle/input/ubc-ocean-dataset/test.csv'"],"ename":"FileNotFoundError","evalue":"[Errno 2] No such file or directory: '/kaggle/input/ubc-ocean-dataset/test.csv'","output_type":"error"}],"execution_count":18},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/competitions/UBC-OCEAN/test.csv')\nis_submission = df.shape[0] != 1\ndf['image_type'] = 'wsi'\ndf.loc[(df['image_width'] < 4000) & (df['image_height'] < 4000), 'image_type'] = 'tma'\ndf['image_path'] = df['image_id'].apply(lambda x: str(competition_dataset / 'test_images' / f'{str(x)}.png'))\nprint(f'Dataset Shape: {df.shape}')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:58:45.143698Z","iopub.execute_input":"2026-03-25T19:58:45.144033Z","iopub.status.idle":"2026-03-25T19:58:45.243562Z","shell.execute_reply.started":"2026-03-25T19:58:45.144003Z","shell.execute_reply":"2026-03-25T19:58:45.242428Z"}},"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/kaggle/input/competitions/UBC-OCEAN/test.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m is_submission \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m      3\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimage_type\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwsi\u001b[39m\u001b[38;5;124m'\u001b[39m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m   1014\u001b[0m     dialect,\n\u001b[1;32m   1015\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m   1023\u001b[0m )\n\u001b[1;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1878\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1879\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1887\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    869\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    870\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    871\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    872\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    880\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    881\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    882\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/kaggle/input/competitions/UBC-OCEAN/test.csv'"],"ename":"FileNotFoundError","evalue":"[Errno 2] No such file or directory: '/kaggle/input/competitions/UBC-OCEAN/test.csv'","output_type":"error"}],"execution_count":14},{"cell_type":"code","source":"def read_image(image_path):\n    image = pyvips.Image.new_from_file(image_path, access='sequential')\n    return np.ndarray(\n        buffer=image.write_to_memory(),\n        dtype=np.uint8,\n        shape=[image.height, image.width, image.bands]\n    )\ndef resize_with_aspect_ratio(image, longest_edge, interpolation=cv2.INTER_LINEAR):\n    height, width = image.shape[:2]\n    scale = longest_edge / max(height, width)\n    image = cv2.resize(image, dsize=(int(np.ceil(width * scale)), int(np.ceil(height * scale))), interpolation=interpolation)\n    return image\ndef drop_low_std(image, threshold):\n    vertical_stds = image.std(axis=(1, 2))\n    horizontal_stds = image.std(axis=(0, 2))\n    cropped_image = image[vertical_stds > threshold, :, :]\n    cropped_image = cropped_image[:, horizontal_stds > threshold, :]\n    return cropped_image\ndef get_largest_contour(image, threshold):\n    image_shape = image.shape[:2]\n    image = cv2.threshold(image, threshold, 255, cv2.THRESH_BINARY)[1]\n    contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n    del image\n    if len(contours) == 0:\n        x1 = 0\n        x2 = image_shape[1] + 1\n        y1 = 0\n        y2 = image_shape[0] + 1\n    else:\n        contour = max(contours, key=cv2.contourArea)\n        mask = np.zeros(image_shape, np.uint8)\n        cv2.drawContours(mask, [contour], -1, 255, cv2.FILLED)\n        y1, y2 = np.min(contour[:, :, 1]), np.max(contour[:, :, 1])\n        x1, x2 = np.min(contour[:, :, 0]), np.max(contour[:, :, 0])\n        x1 = int(0.999 * x1)\n        x2 = int(1.001 * x2)\n        y1 = int(0.999 * y1)\n        y2 = int(1.001 * y2)\n    bounding_box = [x1, y1, min(x2, image_shape[1]), min(y2, image_shape[0])]\n    return bounding_box","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.369036Z","iopub.status.idle":"2026-03-25T19:54:08.369362Z","shell.execute_reply.started":"2026-03-25T19:54:08.369175Z","shell.execute_reply":"2026-03-25T19:54:08.369189Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def visualize_image(image, title, mask=None, path=None):\n    fig, ax = plt.subplots(figsize=(8, 8))\n    ax.imshow(image)\n    if mask is not None:\n        ax.imshow(mask, alpha=0.5)\n    ax.set_xlabel('')\n    ax.set_ylabel('')\n    ax.tick_params(axis='x', labelsize=15, pad=10)\n    ax.tick_params(axis='y', labelsize=15, pad=10)\n    ax.set_title(title, size=15, pad=12.5, loc='center', wrap=True)\n    if path is None:\n        plt.show()\n    else:\n        plt.savefig(path)\n        plt.close(fig)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.370494Z","iopub.status.idle":"2026-03-25T19:54:08.370928Z","shell.execute_reply.started":"2026-03-25T19:54:08.370706Z","shell.execute_reply":"2026-03-25T19:54:08.370729Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class Decoder(nn.Module):\n\n    def __init__(self, encoder_dim=(32, 64, 128, 256), upscale=4, num_classes=1):\n\n        super(Decoder, self).__init__()\n\n        self.conv = nn.ModuleList([\n            nn.Sequential(\n                nn.Conv2d(encoder_dim[i] + encoder_dim[i - 1], encoder_dim[i - 1], 3, 1, 1, bias=False),\n                nn.BatchNorm2d(encoder_dim[i - 1]),\n                nn.ReLU(inplace=True)\n            ) for i in range(1, len(encoder_dim))\n        ])\n\n        self.logit = nn.Conv2d(encoder_dim[0], num_classes, 1, 1, 0)\n        self.up = nn.Upsample(scale_factor=upscale, mode='bilinear')\n\n    def forward(self, feature):\n\n        for i in range(len(feature) - 1, 0, -1):\n            f_up = F.interpolate(feature[i], scale_factor=2, mode='bilinear')\n            f = torch.cat([feature[i - 1], f_up], dim=1)\n            f_down = self.conv[i - 1](f)\n            feature[i - 1] = f_down\n\n        x = self.logit(feature[0])\n        out = self.up(x)\n\n        return out\n\n\nclass SegModel(nn.Module):\n\n    def __init__(self, num_classes):\n\n        super(SegModel, self).__init__()\n\n        self.encoder = timm.create_model(\n            'maxvit_tiny_tf_512.in1k',\n            features_only=True,\n            pretrained=False,\n            out_indices=[1, 2, 3, 4]\n        )\n\n        self.decoder = Decoder(\n            self.encoder.feature_info.channels(),\n            upscale=self.encoder.feature_info.reduction()[0],\n            num_classes=num_classes\n        )\n\n    def forward(self, x):\n\n        feat_maps = self.encoder(x)\n        logits = self.decoder(feat_maps)\n        masks = torch.sigmoid(logits)\n\n        return masks","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.372315Z","iopub.status.idle":"2026-03-25T19:54:08.372747Z","shell.execute_reply.started":"2026-03-25T19:54:08.37251Z","shell.execute_reply":"2026-03-25T19:54:08.37254Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class ClassificationHead(nn.Module):\n\n    def __init__(self, input_dimensions, cancer_output_dimensions):\n\n        super(ClassificationHead, self).__init__()\n\n        self.cancer_head = nn.Linear(input_dimensions, cancer_output_dimensions, bias=True)\n\n    def forward(self, x):\n\n        cancer_output = self.cancer_head(x)\n\n        return cancer_output\n\n\nclass TimmConvImageClassificationModel(nn.Module):\n\n    def __init__(self, model_name, pretrained, backbone_args, pooling_type, dropout_rate, freeze_parameters, head_args):\n\n        super(TimmConvImageClassificationModel, self).__init__()\n\n        self.backbone = timm.create_model(\n            model_name=model_name,\n            pretrained=pretrained,\n            **backbone_args\n        )\n\n        if freeze_parameters:\n            for parameter in self.backbone.parameters():\n                parameter.requires_grad = False\n\n        input_features = self.backbone.get_classifier().in_features\n\n        self.pooling_type = pooling_type\n        self.dropout = nn.Dropout(dropout_rate) if dropout_rate > 0 else nn.Identity()\n        self.head = ClassificationHead(input_dimensions=input_features, **head_args)\n\n    def forward(self, x):\n\n        x = self.backbone.forward_features(x)\n\n        if self.pooling_type == 'avg':\n            x = F.adaptive_avg_pool2d(x, output_size=(1, 1)).view(x.size(0), -1)\n        elif self.pooling_type == 'max':\n            x = F.adaptive_max_pool2d(x, output_size=(1, 1)).view(x.size(0), -1)\n        elif self.pooling_type == 'concat':\n            x = torch.cat([\n                F.adaptive_avg_pool2d(x, output_size=(1, 1)).view(x.size(0), -1),\n                F.adaptive_max_pool2d(x, output_size=(1, 1)).view(x.size(0), -1)\n            ], dim=-1)\n\n        x = self.dropout(x)\n        cancer_output = self.head(x)\n\n        return cancer_output","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.374143Z","iopub.status.idle":"2026-03-25T19:54:08.374586Z","shell.execute_reply.started":"2026-03-25T19:54:08.374356Z","shell.execute_reply":"2026-03-25T19:54:08.374378Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def load_segmentation_model(model_directory, model_file_name, device):\n    model = SegModel(num_classes=1)\n    model.load_state_dict(torch.load(model_directory / model_file_name), strict=True)\n    model.to(device)\n    model.eval()\n    print(f'{model.__class__.__name__} model\\'s weights are loaded from {model_directory / model_file_name}')\n\n    return model\n\n\ndef load_classification_model(model_directory, model_file_names, device):\n\n    config = yaml.load(open(model_directory / 'config.yaml', 'r'), Loader=yaml.FullLoader)\n    config['model']['model_args']['pretrained'] = False\n        \n    models = {}\n\n    for model_file_name in model_file_names:\n        model = eval(config['model']['model_class'])(**config['model']['model_args'])\n        model.load_state_dict(torch.load(model_directory / model_file_name))\n        model.to(device)\n        model.eval()\n        models[model_file_name] = model\n        print(f'{model.__class__.__name__} model\\'s weights are loaded from {model_directory / model_file_name}')\n\n    return models, config\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.376113Z","iopub.status.idle":"2026-03-25T19:54:08.376571Z","shell.execute_reply.started":"2026-03-25T19:54:08.376339Z","shell.execute_reply":"2026-03-25T19:54:08.376362Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"segmentation_model = load_segmentation_model(\n    model_directory=external_dataset / 'segmentation',\n    model_file_name='maxvit_tiny_512_v1_final_epoch_13.pt',\n    device=torch.device('cuda')\n)\n\nmodels, config = load_classification_model(\n    model_directory=external_dataset / 'efficientnetv2s_1024_crop_16_v2.1',\n    model_file_names=[\n        'model_fold1_epoch_8.pt',\n        'model_fold2_epoch_15.pt',\n        'model_fold3_epoch_15.pt',\n        'model_fold4_epoch_15.pt',\n        'model_fold5_epoch_14.pt',\n    ],\n    device=torch.device('cuda')\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.378569Z","iopub.status.idle":"2026-03-25T19:54:08.379364Z","shell.execute_reply.started":"2026-03-25T19:54:08.379096Z","shell.execute_reply":"2026-03-25T19:54:08.37912Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"segmentation_size = 384\nsegmentation_device = torch.device('cuda')\nsegmentation_amp = True\n\nsegmentation_transforms = A.Compose([\n    A.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),\n    ToTensorV2()\n])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.380683Z","iopub.status.idle":"2026-03-25T19:54:08.381141Z","shell.execute_reply.started":"2026-03-25T19:54:08.380917Z","shell.execute_reply":"2026-03-25T19:54:08.380939Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"classification_crop_size = 1024\nclassification_top_n_crops = 16\n\nclassification_device = torch.device('cuda')\nclassification_amp = True\n\ntta = False\ntta_flip_dimensions = [(2,), (3,), (2, 3)]\n\nclassification_transforms = A.Compose([\n    A.Resize(\n        height=1024,\n        width=1024,\n        interpolation=cv2.INTER_NEAREST,\n        always_apply=True\n    ),\n    A.Normalize(\n        mean=[0.485, 0.456, 0.406],\n        std=[0.229, 0.224, 0.225],\n        max_pixel_value=255,\n        always_apply=True\n    ),\n    ToTensorV2(always_apply=True)\n])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.382499Z","iopub.status.idle":"2026-03-25T19:54:08.382936Z","shell.execute_reply.started":"2026-03-25T19:54:08.382709Z","shell.execute_reply":"2026-03-25T19:54:08.382731Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def get_max_quo(num, div):\n    quo, rem = divmod(num, div)\n    max_quo = quo + 1 if rem > 0 else quo\n    return max_quo","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.384566Z","iopub.status.idle":"2026-03-25T19:54:08.384888Z","shell.execute_reply.started":"2026-03-25T19:54:08.384736Z","shell.execute_reply":"2026-03-25T19:54:08.384751Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"predictions = []\nimage_ids = []\n\nfor idx, row in df.iterrows():\n    \n    image_id = row['image_id']\n    image_type = row['image_type']\n    image_path = row['image_path']\n    \n    if image_type == 'tma':\n\n        image = cv2.imread(image_path)\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n        \n        # Drop low standard deviation rows and columns (white areas with less tissue)\n        image = drop_low_std(image=image, threshold=10)\n        \n        tma_predictions = torch.zeros(1, 6)\n        \n        inputs = classification_transforms(image=image)['image']\n        inputs = inputs.to(classification_device)\n\n        if tta:\n            inputs = torch.stack((\n                inputs,\n                torch.flip(inputs, dims=(1,)),\n                torch.flip(inputs, dims=(2,)),\n                torch.flip(inputs, dims=(1, 2))\n            ), dim=0)\n        else:\n            inputs = torch.unsqueeze(inputs, dim=0)\n\n        for model_idx, models in enumerate([models]):\n            for model in models.values():\n                with torch.no_grad():\n                    if classification_amp:\n                        with torch.autocast(device_type='cuda', dtype=torch.float16):\n                            outputs = model(inputs)\n                    else:\n                        outputs = model(inputs)\n\n                outputs = outputs.cpu()\n                if tta:\n                    outputs = torch.mean(outputs, dim=0)\n                else:\n                    outputs = torch.squeeze(outputs, dim=0)\n\n                tma_predictions += outputs / len(models)\n                \n        tma_predictions = torch.softmax(tma_predictions, dim=-1).numpy()\n        predictions.append(tma_predictions)\n        image_ids.append(np.array([image_id]))\n        \n    else:\n        \n        image_thumbnail_path = str(competition_dataset / 'test_thumbnails' / f'{image_id}_thumbnail.png')\n        image_thumbnail = cv2.imread(image_thumbnail_path)\n        image_thumbnail = cv2.cvtColor(image_thumbnail, cv2.COLOR_BGR2RGB)\n        \n        if is_submission is False:\n            print(f'Image {image_id} {image_thumbnail.shape} thumbnail is loaded from {image_thumbnail_path}')\n            visualize_image(image=image_thumbnail, title=f'Image {image_id} Thumbnail {image_thumbnail.shape}', path='./thumbnail_raw.png')\n            \n        image_thumbnail_height, image_thumbnail_width = image_thumbnail.shape[:2]\n        thumbnail_height_padding = segmentation_size - image_thumbnail_height % segmentation_size\n        thumbnail_width_padding = segmentation_size - image_thumbnail_width % segmentation_size\n        image_thumbnail_padded = np.pad(image_thumbnail, ((0 + 64, thumbnail_height_padding + 64), (0 + 64, thumbnail_width_padding + 64), (0, 0)))\n        mask_padded = np.zeros((image_thumbnail_height + thumbnail_height_padding, image_thumbnail_width + thumbnail_width_padding), dtype=np.float32)\n        \n        if is_submission is False:\n            print(f'Image {image_id} {image_thumbnail_padded.shape} thumbnail and mask {mask_padded.shape} are padded')\n            visualize_image(image=image_thumbnail_padded, title=f'Image {image_id} Thumbnail Padded {image_thumbnail_padded.shape}', path='./thumbnail_padded.png')\n        \n        for height in range((image_thumbnail_height + thumbnail_height_padding) // segmentation_size):\n            height_1, height_2 = height * segmentation_size, (height + 1) * segmentation_size\n            for width in range((image_thumbnail_width + thumbnail_width_padding) // segmentation_size):\n                width_1, width_2 = width * segmentation_size, (width + 1) * segmentation_size\n                \n                image_thumbnail_tile = image_thumbnail_padded[height_1:height_2 + 128, width_1:width_2 + 128]\n                inputs = segmentation_transforms(image=image_thumbnail_tile)['image'].to(segmentation_device)\n                inputs = torch.stack((\n                    inputs,\n                    torch.flip(inputs, dims=(1,)),\n                    torch.flip(inputs, dims=(2,)),\n                    torch.flip(inputs, dims=(1, 2))\n                ), dim=0)\n                \n                with torch.no_grad():\n                    if segmentation_amp:\n                        with torch.autocast(device_type='cuda', dtype=torch.float16):\n                            outputs = segmentation_model(inputs)\n                    else:\n                        outputs = segmentation_model(inputs)\n                    \n                outputs = outputs.cpu()\n                outputs = torch.stack((\n                    outputs[0],\n                    torch.flip(outputs[1], dims=(1,)),\n                    torch.flip(outputs[2], dims=(2,)),\n                    torch.flip(outputs[3], dims=(1, 2)),\n                ), dim=0)\n                outputs = torch.mean(outputs, dim=0).squeeze().cpu().numpy()\n                mask_padded[height_1:height_2, width_1:width_2] = outputs[64:448, 64:448]\n                    \n                if is_submission is False:\n                    print(f'Predicted x ({width_1}-{width_2}) y ({height_1}-{height_2})')\n                        \n        mask = mask_padded[:image_thumbnail_height, :image_thumbnail_width]\n        del mask_padded\n        \n        if is_submission is False:\n            print(f'Image {image_id} {mask.shape} mask is predicted')\n            visualize_image(image=image_thumbnail, title=f'Image {image_id} Thumbnail and Mask {mask.shape}', mask=mask, path='./thumbnail_mask.png')\n            \n        # Extract image size from the image headers\n        image_width, image_height = imagesize.get(image_path)\n        # Cast mask soft predictions to 8-bit integer and upsample\n        mask = np.uint8(mask * 255)\n        mask = cv2.resize(mask, (image_width, image_height), cv2.INTER_NEAREST)\n        \n        if is_submission is False:\n            print(f'Image {image_id} mask {mask.shape} is upsampled')\n            visualize_image(image=mask, title=f'Image {image_id} Mask {mask.shape}', path='./resized_mask.png')\n\n        image_height_max_quotient = get_max_quo(image_height, classification_crop_size)\n        image_width_max_quotient = get_max_quo(image_width, classification_crop_size)\n        crops = []\n\n        for h in range(image_height_max_quotient):\n            for w in range(image_width_max_quotient):\n                width_2, height_2 = min(image_width, (w + 1) * classification_crop_size), min(image_height, (h + 1) * classification_crop_size)\n                width_1, height_1 = width_2 - classification_crop_size, height_2 - classification_crop_size\n\n                mask_crop = mask[height_1:height_2, width_1:width_2]\n                area = np.sum(mask_crop) / 255.0\n                crops.append([area, width_1, height_1, width_2, height_2])\n                \n        del mask\n        image = read_image(image_path=image_path)\n        image_crops = []\n        \n        # Sort crops by mask area in descending order\n        crops = np.array(crops)\n        crops = crops[crops[:, 0].argsort()[::-1]].astype(np.int32)\n        \n        if is_submission is False:\n            print(f'{crops.shape[0]} crops are extracted from the mask')\n        \n        # Crop the raw image and delete it\n        for crop_idx, crop in enumerate(crops[:classification_top_n_crops]):\n            area, width_1, height_1, width_2, height_2 = crop\n            image_crops.append(image[height_1:height_2, width_1:width_2, :].copy())    \n        del image\n                \n        crop_predictions = torch.zeros(classification_top_n_crops, 6)\n        for crop_idx, image_crop in enumerate(image_crops):\n            \n            if is_submission is False:\n                print(f'Image {image_id} crop {crop_idx}')\n                visualize_image(image=image_crop, title=f'Image {image_id} Crop {crop_idx}', path=f'./cropped_{crop_idx}.png')\n\n            inputs = classification_transforms(image=image_crop)['image']\n            inputs = inputs.to(classification_device)\n\n            if tta:\n                inputs = torch.stack((\n                    inputs,\n                    torch.flip(inputs, dims=(1,)),\n                    torch.flip(inputs, dims=(2,)),\n                    torch.flip(inputs, dims=(1, 2))\n                ), dim=0)\n            else:\n                inputs = torch.unsqueeze(inputs, dim=0)\n\n            for model_idx, models in enumerate([models]):\n                for model in models.values():\n                    with torch.no_grad():\n                        if classification_amp:\n                            with torch.autocast(device_type='cuda', dtype=torch.float16):\n                                outputs = model(inputs)\n                        else:\n                            outputs = model(inputs)\n\n                    outputs = outputs.cpu()\n                    if tta:\n                        outputs = torch.mean(outputs, dim=0)\n                    else:\n                        outputs = torch.squeeze(outputs, dim=0)\n\n                    crop_predictions[crop_idx] += outputs / len(models)\n\n        crop_predictions = torch.softmax(crop_predictions, dim=-1).numpy()\n        predictions.append(crop_predictions)\n        image_ids.append(np.array([image_id] * classification_top_n_crops))\n        \npredictions = np.concatenate(predictions)\nimage_ids = np.concatenate(image_ids)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.386337Z","iopub.status.idle":"2026-03-25T19:54:08.386641Z","shell.execute_reply.started":"2026-03-25T19:54:08.386494Z","shell.execute_reply":"2026-03-25T19:54:08.386515Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_predictions = pd.DataFrame(data=image_ids, columns=['image_id'])\n\nlabel_mapping = {\n    0: 'HGSC',\n    1: 'EC',\n    2: 'CC',\n    3: 'LGSC',\n    4: 'MC',\n    5: 'Other',\n}\n\nfor label_idx, label in label_mapping.items():\n    df_predictions[f'{label}_prediction'] = predictions[:, label_idx]\n    \ndf_predictions","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.387658Z","iopub.status.idle":"2026-03-25T19:54:08.387941Z","shell.execute_reply.started":"2026-03-25T19:54:08.387803Z","shell.execute_reply":"2026-03-25T19:54:08.387819Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"prediction_columns = [f'{label}_prediction' for label in list(label_mapping.values())]\n\ndf_predictions = df_predictions.groupby('image_id')[prediction_columns].mean().reset_index()\ndf_predictions\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.389934Z","iopub.status.idle":"2026-03-25T19:54:08.390278Z","shell.execute_reply.started":"2026-03-25T19:54:08.390104Z","shell.execute_reply":"2026-03-25T19:54:08.390119Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_predictions['label'] = np.argmax(df_predictions[prediction_columns], axis=-1)\ndf_predictions['label'] = df_predictions['label'].map(label_mapping)\n\ndf_predictions","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.39147Z","iopub.status.idle":"2026-03-25T19:54:08.391745Z","shell.execute_reply.started":"2026-03-25T19:54:08.391607Z","shell.execute_reply":"2026-03-25T19:54:08.391621Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_submission = pd.read_csv(competition_dataset / 'sample_submission.csv').drop(columns=['label'])\ndf_submission","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.394009Z","iopub.status.idle":"2026-03-25T19:54:08.394341Z","shell.execute_reply.started":"2026-03-25T19:54:08.39416Z","shell.execute_reply":"2026-03-25T19:54:08.394175Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_submission = df_submission.merge(df_predictions.loc[:, ['image_id', 'label']], on='image_id', how='left')\ndf_submission","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.395778Z","iopub.status.idle":"2026-03-25T19:54:08.396048Z","shell.execute_reply.started":"2026-03-25T19:54:08.39592Z","shell.execute_reply":"2026-03-25T19:54:08.395933Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_submission.to_csv('submission.csv', index=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.397322Z","iopub.status.idle":"2026-03-25T19:54:08.397638Z","shell.execute_reply.started":"2026-03-25T19:54:08.397483Z","shell.execute_reply":"2026-03-25T19:54:08.397499Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.metrics import (\n    accuracy_score,\n    precision_score,\n    recall_score,\n    f1_score,\n    roc_auc_score,\n    confusion_matrix,\n)\nimport matplotlib.pyplot as plt\nlabel_mapping = {\n    0: \"HGSC\",\n    1: \"EC\",\n    2: \"CC\",\n    3: \"LGSC\",\n    4: \"MC\",\n    5: \"Other\",\n}\ny_one_hot = to_categorical(y, num_classes=6)\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\ntest_image_ids = [f\"img_{i+1}\" for i in range(len(y_true))]  # Simulated unique image IDs\ndf_predictions = pd.DataFrame({\n    \"image_id\": test_image_ids,\n    **{f\"{label}_prediction\": y_pred_probs[:, idx] for idx, label in label_mapping.items()}\n})\ndf_predictions['label_idx'] = np.argmax(df_predictions[[f\"{label}_prediction\" for label in label_mapping.values()]].values, axis=1)\ndf_predictions['label'] = df_predictions['label_idx'].map(label_mapping)\naccuracy = accuracy_score(y_true, y_pred)\nprecision = precision_score(y_true, y_pred, average='weighted')\nrecall = recall_score(y_true, y_pred, average='weighted')\nf1 = f1_score(y_true, y_pred, average='weighted')\nroc_auc = roc_auc_score(y_test, y_pred_probs, multi_class='ovr', average='macro')\nprint(f\"Accuracy: {accuracy:.4f}\")\nprint(f\"Precision: {precision:.4f}\")\nprint(f\"Recall: {recall:.4f}\")\nprint(f\"F1-score: {f1:.4f}\")\nprint(f\"ROC AUC Score (macro-average): {roc_auc:.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.398826Z","iopub.status.idle":"2026-03-25T19:54:08.399085Z","shell.execute_reply.started":"2026-03-25T19:54:08.398956Z","shell.execute_reply":"2026-03-25T19:54:08.39897Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.metrics import accuracy_score\naccuracy = accuracy_score(y_true, y_pred)\nprint(f\"Accuracy: {accuracy:.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.400498Z","iopub.status.idle":"2026-03-25T19:54:08.400763Z","shell.execute_reply.started":"2026-03-25T19:54:08.400632Z","shell.execute_reply":"2026-03-25T19:54:08.400646Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.metrics import precision_score\nprecision = precision_score(y_true, y_pred, average='weighted')\nprint(f\"Precision: {precision:.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.401486Z","iopub.status.idle":"2026-03-25T19:54:08.401748Z","shell.execute_reply.started":"2026-03-25T19:54:08.401618Z","shell.execute_reply":"2026-03-25T19:54:08.401632Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.metrics import recall_score\nrecall = recall_score(y_true, y_pred, average='weighted')\nprint(f\"Recall: {recall:.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.402705Z","iopub.status.idle":"2026-03-25T19:54:08.402991Z","shell.execute_reply.started":"2026-03-25T19:54:08.402853Z","shell.execute_reply":"2026-03-25T19:54:08.402868Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.metrics import f1_score\nf1 = f1_score(y_true, y_pred, average='weighted')\nprint(f\"F1-score: {f1:.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.404456Z","iopub.status.idle":"2026-03-25T19:54:08.404757Z","shell.execute_reply.started":"2026-03-25T19:54:08.404615Z","shell.execute_reply":"2026-03-25T19:54:08.40463Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.metrics import roc_auc_score\nroc_auc = roc_auc_score(y_true, y_pred_probs, multi_class='ovr', average='macro')\nprint(f\"ROC AUC Score (macro-average): {roc_auc:.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.406038Z","iopub.status.idle":"2026-03-25T19:54:08.406483Z","shell.execute_reply.started":"2026-03-25T19:54:08.406254Z","shell.execute_reply":"2026-03-25T19:54:08.406278Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\nimport matplotlib.pyplot as plt\nimport numpy as np\nlabel_mapping = {0: \"CC\", 1: \"EC\", 2: \"HGSC\", 3: \"LGSC\", 4: \"MC\", 5: \"Other\"}\ncm = confusion_matrix(y_true, y_pred)\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[label_mapping[i] for i in range(len(label_mapping))])\ndisp.plot(cmap=plt.cm.Blues)\nplt.title(\"Confusion Matrix\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.407816Z","iopub.status.idle":"2026-03-25T19:54:08.408086Z","shell.execute_reply.started":"2026-03-25T19:54:08.407957Z","shell.execute_reply":"2026-03-25T19:54:08.40797Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.metrics import roc_curve, RocCurveDisplay\nimport matplotlib.pyplot as plt\nfig, ax = plt.subplots(figsize=(10, 8))\nfor i, label in label_mapping.items():\n    fpr, tpr, _ = roc_curve(y_true, y_pred_probs[:, i], pos_label=i)\n    RocCurveDisplay(fpr=fpr, tpr=tpr, estimator_name=label).plot(ax=ax)\nplt.title(\"ROC Curves for Each Class\")\nplt.xlabel(\"False Positive Rate\")\nplt.ylabel(\"True Positive Rate\")\nplt.legend(label_mapping.values())\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.409323Z","iopub.status.idle":"2026-03-25T19:54:08.409607Z","shell.execute_reply.started":"2026-03-25T19:54:08.409462Z","shell.execute_reply":"2026-03-25T19:54:08.409476Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.metrics import matthews_corrcoef\nmcc = matthews_corrcoef(y_true, y_pred)\nprint(f\"Matthews Correlation Coefficient (MCC): {mcc:.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.410985Z","iopub.status.idle":"2026-03-25T19:54:08.411428Z","shell.execute_reply.started":"2026-03-25T19:54:08.411187Z","shell.execute_reply":"2026-03-25T19:54:08.411224Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.metrics import cohen_kappa_score\nkappa = cohen_kappa_score(y_true, y_pred)\nprint(f\"Cohen’s Kappa Score: {kappa:.4f}\")","metadata":{"trusted":true,"jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2026-03-25T19:54:08.413033Z","iopub.status.idle":"2026-03-25T19:54:08.413479Z","shell.execute_reply.started":"2026-03-25T19:54:08.41325Z","shell.execute_reply":"2026-03-25T19:54:08.413273Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.metrics import balanced_accuracy_score\nbalanced_accuracy = balanced_accuracy_score(y_true, y_pred)\nprint(f\"Balanced Accuracy: {balanced_accuracy:.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-25T19:54:08.414688Z","iopub.status.idle":"2026-03-25T19:54:08.415113Z","shell.execute_reply.started":"2026-03-25T19:54:08.414887Z","shell.execute_reply":"2026-03-25T19:54:08.414911Z"}},"outputs":[],"execution_count":null}]}